knn.ani()

Demonstrate the process of k-Nearest Neighbour classification on the 2D plane.

For each row of the test set, the \(k\) nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote, with ties broken at random. For a single test sample point, the basic steps are:

• locate the test point
• compute the distances between the test point and all points in the training set
• find \(k\) shortest distances and the corresponding training set points
• vote for the result (find the maximum in the table for the true classifications)

As there are four steps in an iteration, the total number of animation frames should be 4 * min(nrow(test), ani.options('nmax')) at last.

library(animation)
## a binary classification problem
ani.options(interval = 1, nmax = 10)
x = matrix(c(rnorm(80, mean = -1), rnorm(80, mean = 1)), ncol = 2, byrow = TRUE)
y = matrix(rnorm(20, mean = 0, sd = 1.2), ncol = 2)
knn.ani(train = x, test = y, cl = rep(c("first class", "second class"), each = 40),
  k = 30)

plot of chunk demo-a

x = matrix(c(rnorm(30, mean = -2), rnorm(30, mean = 2), rnorm(30, mean = 0)), ncol = 2,
  byrow = TRUE)
y = matrix(rnorm(20, sd = 2), ncol = 2)
knn.ani(train = x, test = y, cl = rep(c("first", "second", "third"), each = 15),
  k = 25, cl.pch = c(2, 3, 19), dist.lty = 3)

plot of chunk demo-b

## an interactive demo: choose the test set by mouse-clicking
if (interactive()) {
  ani.options(nmax = 5)
  knn.ani(interact = TRUE)
}